The overall goal of this project is to develop a safe, broadly effective, and affordable vaccine to prevent group A streptococcal infections. Antibodies against the N-terminal hypervariable region (HVR) of surface M (Emm) proteins of GAS are opsonic and are associated with protection against infection. Immunity has classically been described as ?type-specific?, leading to the assumption that natural immunity confers protection against only one of the more than 200 different emm types of GAS. We now have new information that calls into question this classic view and serves as the basis for an entirely different approach to GAS vaccine design and development. A recent comprehensive sequence analysis of M proteins from a global collection of 175 emm types of GAS resulted in a new emm cluster typing system that classified 96.2% of all contemporary GAS isolates into 48 emm clusters containing structurally and functionally related M proteins. Moreover, 117 emm types contained in 16 clusters accounted for 94.4% of GAS infections in the world. Indeed, preclinical studies indicated that a multivalent vaccine containing N-terminal peptides from 30 prevalent M types cross-opsonized a significant number of non-vaccine emm types of GAS that co-localized in clusters with vaccine emm types. The frequency of cross-opsonic antibodies, combined with the emm cluster data, prompted us to conclude that there is a need for a paradigm shift away from the concept of ?type-specific? immunity against GAS infections to one of ?cluster-specific? immunity. Our overall hypothesis is that immunity to GAS infections is the result of both type-specific and cross-reactive antibodies against the N-terminal regions of M proteins and that a new approach employing computational predictions of peptide structures will result in a multivalent vaccine that will induce broadly protective immunity in populations throughout the world. Our preliminary results indicate the feasibility of using structure-based design to predict the antigenic relatedness of M peptides within a cluster. The specific aims of this proposal are to: 1) Apply computational structure-based design in an iterative process with immunological data from Aim 2 to predict the minimal number of M peptide sequences that are most representative of the structural and physicochemical properties of the peptides in one emm cluster containing 17 GAS emm types, 2) determine the cross-reactive immunogenicity of the selected peptides with all seventeen emm types of GAS in the cluster, and apply the results to refine the computational design predictions in Aim 1, 3) apply the refined computational parameters from Aims 1 and 2 to analyze the remaining epidemiologically important emm clusters, select a comprehensive panel of peptides representing all emm types, construct four multivalent recombinant vaccine proteins, and assess potential cross-protective immunogenicity using in vitro bactericidal assays against all 117 emm types of GAS, and 4) determine the protective immunogenicity of the final multivalent vaccine in unique transgenic mice expressing human C4BP and factor H that will be immunized and then challenged with multiple emm types of GAS.